IDEAS home Printed from https://ideas.repec.org/a/spr/eurase/v15y2025i1d10.1007_s40822-024-00304-9.html
   My bibliography  Save this article

The relationships between RedditSI and BTC exchange characteristics: Do Reddit users still control the market?

Author

Listed:
  • Valeriia Baklanova

    (HSE University)

Abstract

This study investigates the influence of Reddit community on Bitcoin market performance by introducing the Reddit Sentiment Index (RedditSI) as a tool to measure sentiment among Reddit users. The index was crafted based on the Bitcoin-related subreddits and classified with the Flair NLP model. Statistical analysis, including correlation, cointegration and causality tests, revealed significant relationships between RedditSI and BTC exchange characteristics (price, returns, absolute returns, volatility and volume), both in the short and long term. The findings highlight the importance of ongoing sentiment monitoring for investors, regulators and researchers to better understand the link between human psychology and cryptocurrency markets.

Suggested Citation

  • Valeriia Baklanova, 2025. "The relationships between RedditSI and BTC exchange characteristics: Do Reddit users still control the market?," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 15(1), pages 285-306, March.
    • Handle: RePEc:spr:eurase:v:15:y:2025:i:1:d:10.1007_s40822-024-00304-9
    DOI: 10.1007/s40822-024-00304-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40822-024-00304-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s40822-024-00304-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Shen, Dehua & Urquhart, Andrew & Wang, Pengfei, 2019. "Does twitter predict Bitcoin?," Economics Letters, Elsevier, vol. 174(C), pages 118-122.
    2. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 39(3), pages 106-135.
    3. Sergey Nasekin & Cathy Yi-Hsuan Chen, 2020. "Deep learning-based cryptocurrency sentiment construction," Digital Finance, Springer, vol. 2(1), pages 39-67, September.
    4. Trimborn, Simon & Härdle, Wolfgang Karl, 2018. "CRIX an Index for cryptocurrencies," Journal of Empirical Finance, Elsevier, vol. 49(C), pages 107-122.
    5. Almeida, José & Gonçalves, Tiago Cruz, 2023. "A systematic literature review of investor behavior in the cryptocurrency markets," Journal of Behavioral and Experimental Finance, Elsevier, vol. 37(C).
    6. Telli, Şahin & Chen, Hongzhuan, 2021. "Multifractal behavior relationship between crypto markets and Wikipedia-Reddit online platforms," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    7. Kraaijeveld, Olivier & De Smedt, Johannes, 2020. "The predictive power of public Twitter sentiment for forecasting cryptocurrency prices," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 65(C).
    8. Milos Ciganovic & Federico D’Amario, 2024. "Forecasting cryptocurrencies log-returns: a LASSO-VAR and sentiment approach," Applied Economics, Taylor & Francis Journals, vol. 56(58), pages 8112-8138, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Moser, Stefanie & Brauneis, Alexander, 2023. "Should you listen to crypto YouTubers?," Finance Research Letters, Elsevier, vol. 54(C).
    2. Suwan (Cheng) Long & Ioannis Chatziantoniou & David Gabauer & Brian Lucey, 2024. "Do social media sentiments drive cryptocurrency intraday price volatility? New evidence from asymmetric TVP-VAR frequency connectedness measures," The European Journal of Finance, Taylor & Francis Journals, vol. 30(13), pages 1470-1489, September.
    3. Fayssal Jamhamed & Franck Martin & Fabien Rondeau & Josué Thélissaint & Stéphane Tufféry, 2024. "Regime-Specific Dynamics and Informational Efficiency in Cryptomarkets: Evidence from Gaussian Mixture Models," Economics Working Paper Archive (University of Rennes & University of Caen) 2024-13, Center for Research in Economics and Management (CREM), University of Rennes, University of Caen and CNRS.
    4. Wang, Yizhi & Lucey, Brian M. & Vigne, Samuel A. & Yarovaya, Larisa, 2022. "The Effects of Central Bank Digital Currencies News on Financial Markets," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    5. Bouteska, Ahmed & Mefteh-Wali, Salma & Dang, Trung, 2022. "Predictive power of investor sentiment for Bitcoin returns: Evidence from COVID-19 pandemic," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    6. Mercik, Aleksander & Słoński, Tomasz & Karaś, Marta, 2024. "Understanding crypto-asset exposure: An investigation of its impact on performance and stock sensitivity among listed companies," International Review of Financial Analysis, Elsevier, vol. 92(C).
    7. Wang, Qiyu & Chong, Terence Tai-Leung, 2021. "Factor pricing of cryptocurrencies," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    8. Flori, Andrea, 2019. "News and subjective beliefs: A Bayesian approach to Bitcoin investments," Research in International Business and Finance, Elsevier, vol. 50(C), pages 336-356.
    9. Bennett, Donyetta & Mekelburg, Erik & Strauss, Jack & Williams, T.H., 2024. "Unlocking the black box of sentiment and cryptocurrency: What, which, why, when and how?," Global Finance Journal, Elsevier, vol. 60(C).
    10. Dunbar, Kwamie & Owusu-Amoako, Johnson, 2023. "Predictability of crypto returns: The impact of trading behavior," Journal of Behavioral and Experimental Finance, Elsevier, vol. 39(C).
    11. Daeyun Kang & Doojin Ryu & Robert I. Webb, 2025. "Bitcoin as a financial asset: a survey," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-28, December.
    12. Zribi, Wissal & Boufateh, Talel & Guesmi, Khaled, 2023. "Climate uncertainty effects on bitcoin ecological footprint through cryptocurrency environmental attention," Finance Research Letters, Elsevier, vol. 58(PD).
    13. Zuo Xiaorui & Chen Yao-Tsung & Härdle Wolfgang Karl, 2024. "Emoji driven crypto assets market reactions," Management & Marketing, Sciendo, vol. 19(2), pages 158-178.
    14. Kamyr Gomes Souza & Flavio Barboza & Daniel Vitor Tartari Garruti, 2024. "A Discourse Analysis of Tweets and Its Implications for Cryptocurrency Prices and Trade Volumes," Computational Economics, Springer;Society for Computational Economics, vol. 64(4), pages 2355-2383, October.
    15. Andrew Phiri, 2022. "Can wavelets produce a clearer picture of weak-form market efficiency in Bitcoin?," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 12(3), pages 373-386, September.
    16. Ardia, David & Bluteau, Keven, 2024. "Twitter and cryptocurrency pump-and-dumps," International Review of Financial Analysis, Elsevier, vol. 95(PB).
    17. Georg Keilbar & Yanfen Zhang, 2021. "On cointegration and cryptocurrency dynamics," Digital Finance, Springer, vol. 3(1), pages 1-23, March.
    18. Clark, Ephraim & Lahiani, Amine & Mefteh-Wali, Salma, 2023. "Cryptocurrency return predictability: What is the role of the environment?," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    19. Zdravka Aljinović & Branka Marasović & Tea Šestanović, 2021. "Cryptocurrency Portfolio Selection—A Multicriteria Approach," Mathematics, MDPI, vol. 9(14), pages 1-21, July.
    20. Wang, Fang & Gacesa, Marko, 2023. "Semi-strong efficient market of Bitcoin and Twitter: An analysis of semantic vector spaces of extracted keywords and light gradient boosting machine models," International Review of Financial Analysis, Elsevier, vol. 88(C).

    More about this item

    Keywords

    Bitcoin (BTC); Investor sentiment; Natural language processing; Time series analysis;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:eurase:v:15:y:2025:i:1:d:10.1007_s40822-024-00304-9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.